{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:CA6EUA3HKMWHPOHWU2XP2JDGWL","short_pith_number":"pith:CA6EUA3H","schema_version":"1.0","canonical_sha256":"103c4a0367532c77b8f6a6aefd2466b2fa3cda6adfb7c2882e10cc24a67c4228","source":{"kind":"arxiv","id":"1601.01422","version":1},"attestation_state":"computed","paper":{"title":"Stochastic Dykstra Algorithms for Metric Learning on Positive Semi-Definite Cone","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jun Sese, Raissa Relator, Tomoki Matsuzawa, Tsuyoshi Kato","submitted_at":"2016-01-07T07:29:45Z","abstract_excerpt":"Recently, covariance descriptors have received much attention as powerful representations of set of points. In this research, we present a new metric learning algorithm for covariance descriptors based on the Dykstra algorithm, in which the current solution is projected onto a half-space at each iteration, and runs at O(n^3) time. We empirically demonstrate that randomizing the order of half-spaces in our Dykstra-based algorithm significantly accelerates the convergence to the optimal solution. Furthermore, we show that our approach yields promising experimental results on pattern recognition "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1601.01422","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-01-07T07:29:45Z","cross_cats_sorted":[],"title_canon_sha256":"afa189abb19892ff8e8b589a3e0e84b1b602ad956ab4212ece1ee4e6cda188fa","abstract_canon_sha256":"9964240c5529e94995e1af590ae9f74aa333079d4fd7559df4979fef3e4f59ae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:23:13.724427Z","signature_b64":"gKhFqr5YkiZzO6Z0kLDabO4cb4t/x7qLBrRp8d40E+fuZ+3qfTEzp7T8k7PMHiW45WlcEdc3NNnQz2jb2l71Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"103c4a0367532c77b8f6a6aefd2466b2fa3cda6adfb7c2882e10cc24a67c4228","last_reissued_at":"2026-05-18T01:23:13.723746Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:23:13.723746Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Stochastic Dykstra Algorithms for Metric Learning on Positive Semi-Definite Cone","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jun Sese, Raissa Relator, Tomoki Matsuzawa, Tsuyoshi Kato","submitted_at":"2016-01-07T07:29:45Z","abstract_excerpt":"Recently, covariance descriptors have received much attention as powerful representations of set of points. In this research, we present a new metric learning algorithm for covariance descriptors based on the Dykstra algorithm, in which the current solution is projected onto a half-space at each iteration, and runs at O(n^3) time. We empirically demonstrate that randomizing the order of half-spaces in our Dykstra-based algorithm significantly accelerates the convergence to the optimal solution. Furthermore, we show that our approach yields promising experimental results on pattern recognition "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1601.01422","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1601.01422","created_at":"2026-05-18T01:23:13.723841+00:00"},{"alias_kind":"arxiv_version","alias_value":"1601.01422v1","created_at":"2026-05-18T01:23:13.723841+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1601.01422","created_at":"2026-05-18T01:23:13.723841+00:00"},{"alias_kind":"pith_short_12","alias_value":"CA6EUA3HKMWH","created_at":"2026-05-18T12:30:09.641336+00:00"},{"alias_kind":"pith_short_16","alias_value":"CA6EUA3HKMWHPOHW","created_at":"2026-05-18T12:30:09.641336+00:00"},{"alias_kind":"pith_short_8","alias_value":"CA6EUA3H","created_at":"2026-05-18T12:30:09.641336+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CA6EUA3HKMWHPOHWU2XP2JDGWL","json":"https://pith.science/pith/CA6EUA3HKMWHPOHWU2XP2JDGWL.json","graph_json":"https://pith.science/api/pith-number/CA6EUA3HKMWHPOHWU2XP2JDGWL/graph.json","events_json":"https://pith.science/api/pith-number/CA6EUA3HKMWHPOHWU2XP2JDGWL/events.json","paper":"https://pith.science/paper/CA6EUA3H"},"agent_actions":{"view_html":"https://pith.science/pith/CA6EUA3HKMWHPOHWU2XP2JDGWL","download_json":"https://pith.science/pith/CA6EUA3HKMWHPOHWU2XP2JDGWL.json","view_paper":"https://pith.science/paper/CA6EUA3H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1601.01422&json=true","fetch_graph":"https://pith.science/api/pith-number/CA6EUA3HKMWHPOHWU2XP2JDGWL/graph.json","fetch_events":"https://pith.science/api/pith-number/CA6EUA3HKMWHPOHWU2XP2JDGWL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CA6EUA3HKMWHPOHWU2XP2JDGWL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CA6EUA3HKMWHPOHWU2XP2JDGWL/action/storage_attestation","attest_author":"https://pith.science/pith/CA6EUA3HKMWHPOHWU2XP2JDGWL/action/author_attestation","sign_citation":"https://pith.science/pith/CA6EUA3HKMWHPOHWU2XP2JDGWL/action/citation_signature","submit_replication":"https://pith.science/pith/CA6EUA3HKMWHPOHWU2XP2JDGWL/action/replication_record"}},"created_at":"2026-05-18T01:23:13.723841+00:00","updated_at":"2026-05-18T01:23:13.723841+00:00"}